publications

2026

1. Narrative Networks as Meta-Representations: A Survey and Typology of Automated Extraction in Computational Linguistics

   Devin R. Wright

   2026

   Under review

2. 

   Language Predicts Identity Fusion Across Cultures and Reveals Divergent Pathways to Violence

   Devin R. Wright, Justin E. Lane, and F. LeRon Shults

   2026

   Submitted. Preprint available on arXiv.

   arXiv
3. From Noise to Signal: Identity Fusion from Language at Scale

   Devin R. Wright, Justin E. Lane, Jisun An, and 2 more authors

   10th Biennial Meeting of the International Association for the Cognitive and Evolutionary Sciences of Religion, Brno, Czech Republic, Jul 2026

   (Talk)

2025

1. 

   Sameness entices, but novelty enchants in fanfiction online

   Elise Jing, Simon DeDeo, Devin Robert Wright, and 1 more author

   Humanities and Social Sciences Communications, Jul 2025

   Abs DOI URL

   Cultural evolution is driven by how we choose what to consume and share with others. A common belief is that the cultural artifacts that succeed are the ones that balance novelty and conventionality. This “balance theory” suggests that people prefer works that are familiar, but not so familiar as to be boring; novel, but not so novel as to violate the expectations of their genre. We test this idea using a large dataset of fanfiction, a unique data source that mitigates many common critical shortcomings in the study of creative works. We apply a multiple regression model and a generalized additive model to examine how the recognition a work receives varies with its novelty, estimated through a Latent Dirichlet Allocation topic model, in the context of existing works. We find the opposite pattern of what the balance theory predicts—overall success declines almost monotonically with novelty and exhibits a U-shaped instead of an inverse U-shaped curve. This puzzle is resolved by teasing out two competing forces: sameness attracts the masses whereas novelty provides enjoyment. Taken together, even though the balance theory holds in terms of expressed enjoyment, the overall success can show the opposite pattern due to the dominant role of familiarity to attract the audience. Under these two “forces”, cultural evolution may have to work against inertia—the appetite for consuming the familiar—and may resemble a punctuated equilibrium, marked by occasional leaps.

2. When “We” Means Me: AI, Identity Fusion, and Violence Risk

   Devin R. Wright, Jisun An, and Yong-Yeol Ahn

   Cognitive Lunch Seminar Series, Bloomington, Indiana, Apr 2025

   (Talk)

3. 

   Cognitive Linguistic Identity Fusion Score (CLIFS): A Scalable Cognition-Informed Approach to Quantifying Identity Fusion from Text

   Devin R. Wright, Jisun An, and Yong-Yeol Ahn

   In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, Nov 2025

   Abs DOI URL

   Quantifying *identity fusion*—the psychological merging of self with another entity or abstract target (e.g., a religious group, political party, ideology, value, brand, belief, etc.)—is vital for understanding a wide range of group-based human behaviors. We introduce the Cognitive Linguistic Identity Fusion Score ([CLIFS](https://github.com/DevinW-sudo/CLIFS)), a novel metric that integrates cognitive linguistics with large language models (LLMs), which builds on implicit metaphor detection. Unlike traditional pictorial and verbal scales, which require controlled surveys or direct field contact, CLIFS delivers fully automated, scalable assessments while maintaining strong alignment with the established verbal measure. In benchmarks, CLIFS outperforms both existing automated approaches and human annotation. As a proof of concept, we apply CLIFS to violence risk assessment to demonstrate that it can improve violence risk assessment by more than 240%. Building on our identification of a new NLP task and early success, we underscore the need to develop larger, more diverse datasets that encompass additional fusion-target domains and cultural backgrounds to enhance generalizability and further advance this emerging area. CLIFS models and code are public at [https://github.com/DevinW-sudo/CLIFS](https://github.com/DevinW-sudo/CLIFS).

2024

1. 

   Humans create more novelty than ChatGPT when asked to retell a story

   Fritz Breithaupt, Ege Otenen, Devin R. Wright, and 3 more authors

   Scientific Reports, Jan 2024

   Fritz Breithaupt, Ege Otenen, and Devin R. Wright are equal first authors.

   Abs DOI Supp

   We compare how humans retell stories to how ChatGPT retells stories in chains of three retellings by different people or different accounts on ChatGPT. ChatGPT provides competent summaries of the original narrative texts in one step of retelling. In subsequent retellings few additional changes occur. Human retellers, by contrast, reduce the original text incrementally and by creating 55–60% of novel words and concepts (synsets) at each iteration. The retellings by both ChatGPT and humans show very stable emotion ratings, which is a puzzle for human retellers given the high degree of novel inventions across retellings. ChatGPT maintains more nouns, adjectives, and prepositions and also uses language later acquired in life, while humans use more verbs, adverbs, and negations and use language acquired at a younger age. The results reveal that spontaneous retelling by humans involves ongoing creativity, anchored by emotions, beyond the default probabilistic wording of large language models such as ChatGPT.

2. Exploring the Impact of Factually Erroneous Cultural Mental Models on Community Understanding

   Devin R. Wright

   9th Biennial Meeting of the International Association for the Cognitive and Evolutionary Sciences of Religion, Oxford, United Kingdom, Jun 2024

   (Poster)

2023

1. 

   Man vs. Machine: What ChatGPT Reveals about Human Memory, Emotion, and Creativity in the Serial Reproduction of Stories

   Ege Otenen, Devin Robert Wright, Yiyan Tan, and 3 more authors

   Midwest Cognitive Science Conference, Grand Rapids, Michigan, May 2023

   (Talk)

   URL

2022

1. 

   An autonomous discord bot to improve online course experience and engagement: Lessons learned amid the covid-19 pandemic

   Devin Robert Wright, Timothy Severance, Charles D. Knutson, and 2 more authors

   Proceedings of the IEEE Conference on Software Engineering Education and Training (CSEE&T), Maui, Hawaii, Jan 2022

   PDF

2021

1. Using Natural Language Processing and Machine Learning to Identify Trends in Flight Software Patch History

   Devin Robert Wright, and Gerardo E. Cruz-Ortiz

   NASA Virtual Intern Symposium, Summer 2021, Virtual–Greenbelt, Maryland, Jan 2021

   (Talk)
2. Constructing an Assembler and Virtual Machine

   Devin Robert Wright, and Curtis Ray Welborn

   National Conference on Undergraduate Research 2021@home, Virtual, Jan 2021

   (Poster)

   URL PDF
3. Constructing an Assembler and Virtual Machine

   Devin Robert Wright, and Curtis Ray Welborn

   Utah Conference on Undergraduate Research 2021, hosted by Brigham Young University, Virtual–Provo, Utah, Jan 2021

   (Talk)

2020

1. Improving Plan Specification Language Usability and Applying Language to Model Autonomous Interplanetary Communications

   Devin Robert Wright, Jeremy Frank, and Michael Dalal

   Ames Virtual Intern Poster Symposium and Exit Presentation Series, Virtual–Mountain View, California, Jan 2020

   (Poster)

   URL PDF